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This article tackles how businesses can use this period to build data and AI capabilities that will define their competitive position in the recovery, and for years beyond it.

The conditions creating the pressure — supply chain disruption, energy cost volatility, demand uncertainty, will not be permanent. But the businesses that treat this period purely as something to survive will exit it with a mountain to climb to rebuild to the point of previous levels. The businesses that treat this period as a forcing function to adopt and build lasting capabilities that should have been in place anyway will emerge structurally stronger long into the future.

From crisis response to competitive capability

There’s a meaningful difference between using data to get through a crisis and using data as a permanent management discipline. The first is defensive: dashboards to track what’s happening, models to understand your exposure, scenarios to prepare for a range of outcomes. That work is necessary and if you haven’t done it yet, it’s still the starting point.

But the second is where durable advantage actually sits. Businesses that have embedded data-driven decision-making into their operating rhythm don’t just handle disruption better. They make better decisions in ordinary conditions too. They spot opportunities earlier, allocate resource more efficiently, and build the kind of institutional intelligence that compounds over time.

The question this period is forcing every leadership team to ask: “what do we actually know about our own business, and how quickly can we act on it?”, is one that should have been asked in calmer times. The opportunity now is to answer it properly, and to build the infrastructure that means you never have to ask it in quite the same way again.

The businesses that treat this period as a forcing function to adopt and build lasting capabilities that should have been in place anyway, will emerge structurally stronger long into the future.

Where AI changes the equation

Much of what we covered in our BCCD AI readiness masterclass last year focused on the foundations: data quality, governance, the people and process changes required before AI can deliver consistent value. Those foundations remain essential. But the AI capability landscape has moved materially since then, and where the practical value lies for businesses of all sizes is worth being specific about.

AI as a thinking partner for complex decisions

The most underused application of AI in business right now is analysis. When a large language model has access to your business dataand a well-formed question, it can synthesise information across multiple variables faster than any team and surface 

connections that would take weeks to identify manually. Which customer segments are most sensitive to a price change? Where in your supply chain does a 15% freight cost increase actually break your margin? Which of your service lines is most exposed if a key client pauses spend? 

These aren’t questions that require expensive custom AI development. They’re questions your existing data can answer, connected to AI tools that are already commercially available, if the data is accessible, clean and governed and the question is well-formed. The constraint, almost always, is quality of data.

From reporting to anticipation: the predictive shift

The shift from describing what happened to anticipating what’s likely to happen next is where AI creates its most significant business value. Demand forecasting, churn prediction, supplier risk scoring, cash flow projection under multiple scenarios — each of these is now achievable for businesses that previously would have needed a dedicated data science team to attempt them.

Modern AI-assisted tools, built into platforms your business may already use, or available as relatively lightweight additions to existing infrastructure, can give a mid-size business meaningful predictive capability within months. The maturity curve has compressed dramatically. What took eighteen months to build three years ago can often be achieved in six or less, provided the data foundations are in reasonable shape.

AI that knows your business

One of the most significant practical developments of the past year is the maturation of what the industry calls retrieval-augmented generation, the ability to connect a powerful AI model to your own proprietary data, so that its outputs reflect your specific business context rather than general knowledge.

For a professional services firm, this means AI that can draft proposals drawing on your past work and your specific methodology. For a retailer, AI that can answer stock and pricing questions using your actual inventory and margin data. For a logistics operator, AI that can advise on route decisions using your operational history.

This is the point at which AI becomes a genuine competitive differentiator. And it’s why the quality of your data; its accuracy, its accessibility, its governance, is the single most important determinant of the value you can extract from any AI investment.

Building for the recovery

The current operating environment will not be permanent. Diplomatic efforts are active, the UAE’s economic fundamentals remain sound, and the region’s long-term trajectory as a global business hub is intact. When conditions stabilise, the recovery will be real, and in certain sectors it will be rapid.

However, recovery is never uniform. The businesses that capture the upside most effectively will be those that used this period to build the capabilities they should have had before the disruption. The logic is worth making explicit:

When travel and tourism resume: the hospitality and retail businesses with the sharpest understanding of their customer base, i.e who their most valuable segments are, what drives their behaviour, how to reach them efficiently, will rebuild revenue fastest. Those relying on broad-based marketing to an undifferentiated audience will spend more to recover less.

When supply chains normalise: the businesses that have used this period to map their supplier networks, identify dependencies, and begin building alternative relationships will face fewer disruptions next time. Concentration risk that was invisible in stable  conditions has now been named. The businesses that act on that knowledge carry a structural advantage into the next cycle.

When investor confidence returns fully: the businesses able to demonstrate operational transparency, data-driven decision-making, and clear governance of their AI and data assets will attract capital and partnerships more readily. Institutional investors and corporate acquirers are increasingly sophisticated about data maturity as a proxy for operational quality. The investment you make now in your data infrastructure sits on your balance sheet as well as in your operations.

The capabilities you build under pressure become the advantage you carry into recovery. Data investment compounds.

This is the compounding logic of data capability: it doesn’t just help you through the current disruption. It changes your baseline. The businesses that treat data as a permanent management discipline are consistently better positioned, regardless of what the environment does next.

Three actions for this week

Whatever your sector or scale, these are concrete and achievable right now:

Map your three biggest exposures. Identify your three most significant cost inputs, your three most critical revenue streams, and your three most important supplier or partner relationships. For each, document what has changed and what your response options are. If you can’t complete this in a working day, you have a data accessibility problem worth addressing immediately. That constraint will slow every other decision you need to make.

Define your recovery data priority. Identify the single capability; customer segmentation, demand forecasting, supplier intelligence, cash flow modelling, that would most change how you approach the recovery period. Not a transformation programme. One capability, with a clear use case and a measurable outcome. Map the data you’d need, assess its current quality, and set a six-week delivery target. Most of these are achievable within that window with existing tools.

Start the AI readiness conversation at leadership level. If AI has been discussed primarily as an IT question in your organisation, bring it to the leadership table as a strategy question. Which decisions would you make differently if you had better information faster? Which processes consume leadership time that could be augmented or automated? Which aspects of your competitive differentiation depend on institutional knowledge that currently lives in people rather than systems? These questions have answers. The answers are where your AI roadmap starts.

A final thought

The BCCD community spans businesses at very different stages of data and AI maturity, from organisations still working on the foundations to those already deploying sophisticated predictive tools and advanced AI integrated processes. The message of this piece is the same for all of them: the window between disruption and recovery is one of the most productive times to build, because the case for investment is self-evident and the urgency is real.

We covered the foundations in our AI readiness masterclass last year. We covered the immediate response in our piece on navigating cost uncertainty. This article is about the third stage: using a difficult period to build capabilities that define your position in what comes after it.

We’d love to hear what resonates and where your business is on this journey. Find us at the next BCCD event or reach out directly.

Author: Luke Cann, CEO and Co-Founder, HEMOdata

Luke Cann is CEO of HEMOdata, a UAE-based data and AI solutions consultancy specialising in data strategy, governance, engineering, and AI solutions. HEMOdata works with organisations across the GCC to help businesses build the data and AI foundations that drive better decisions and long-term competitive advantage. Luke is also President of DAMA-DXB, the Dubai chapter of DAMA International.